anysite-audience-analysis

What this skill does

# anysite Audience Analysis

Understand your audience through demographic analysis, engagement patterns, and follower behavior across Instagram, YouTube, and LinkedIn.

## Overview

- **Analyze follower demographics** and characteristics
- **Track engagement patterns** and behavior
- **Evaluate audience quality** and authenticity
- **Identify content preferences** by audience segment
- **Optimize targeting** based on audience insights

**Coverage**: 60% - Focused on Instagram, YouTube, LinkedIn

## Supported Platforms

- ✅ **Instagram**: Follower analysis, engagement patterns, audience location
- ✅ **YouTube**: Subscriber insights, comment demographics, viewer behavior
- ✅ **LinkedIn**: Connection analysis, professional demographics, engagement

## Quick Start

**Step 1: Identify Audience Source**

Choose platform:
- Instagram: `execute("instagram", "user", "user", {"user": "..."})` + `execute("instagram", "user", "user_friendships", {"user": "...", "count": 100, "type": "followers"})`
- YouTube: `execute("youtube", "channel", "channel_videos", {"channel": "...", "count": 50})` + comment analysis
- LinkedIn: `execute("linkedin", "post", "get_user_posts", {"user": "...", "count": 50})` + engagement analysis

**Step 2: Collect Audience Data**

Gather:
- Follower/subscriber counts
- Engagement metrics
- Demographics (from profiles)
- Behavior patterns

**Step 3: Analyze Patterns**

Look for:
- Audience segments
- Engagement drivers
- Content preferences
- Peak activity times

Use `query_cache()` to filter and aggregate cached data without re-fetching.

**Step 4: Generate Insights**

Deliver:
- Audience profile summary
- Engagement patterns
- Content recommendations
- Targeting suggestions

Use `export_data()` to provide downloadable CSV/JSON files.

## Common Workflows

### Workflow 1: Instagram Audience Analysis

**Steps**:

1. **Get Profile Overview**
```
execute("instagram", "user", "user", {"user": "username"})
→ Follower count (follower_count), post count (media_count), bio (description)
→ Fields: id, alias, name, url, image, follower_count, following_count, description, media_count, is_private, is_verified, is_business, category, external_url, email, location
```

2. **Analyze Followers** (sample)
```
execute("instagram", "user", "user_friendships", {
  "user": "username",
  "count": 100,
  "type": "followers"
})
→ Fields: id, name, alias, url, image, is_verified, is_private

For each follower (sample):
- Profile type (personal, business, creator)
- Bio indicators (interests, location)
- Follower count (influence level)

Use get_page(cache_key, offset=10, limit=10) to load more followers.
```

3. **Engagement Pattern Analysis**
```
execute("instagram", "user", "user_posts", {"user": "username", "count": 50})
→ Fields: id, code, url, image, text, created_at, like_count, comment_count, reshare_count, view_count, type, is_paid_partnership

For each post:
  execute("instagram", "post", "post_likes", {"post": "{id}", "count": 100})
  → Fields: id, name, alias, url, image, is_verified, is_private

  execute("instagram", "post", "post_comments", {"post": "{id}", "count": 50})
  → Fields: id, comment_index, created_at, text, like_count, reply_count, parent_id, user

Analyze:
- Who engages most (power users)
- When engagement happens (timing via created_at)
- What content drives engagement
- Comment quality and topics

Use query_cache(cache_key, sort_by={"field": "like_count", "order": "desc"})
to find top-performing posts without re-fetching.
```

4. **Audience Segmentation**
```
Group followers by:
- Engagement level (active, passive, ghost)
- Interests (from bios)
- Location (from profiles)
- Influence (follower counts)

Use query_cache(cache_key, conditions=[{"field": "is_verified", "op": "eq", "value": true}])
to filter verified followers.
```

**Expected Output**:
- Audience demographics summary
- Engagement patterns
- Top engaged followers
- Content preferences

Use `export_data(cache_key, "csv")` to provide a downloadable follower/engagement report.

### Workflow 2: YouTube Audience Insights

**Steps**:

1. **Channel Overview**
```
execute("youtube", "channel", "channel_videos", {"channel": "@channel_alias", "count": 50})
→ Fields: id, title, url, author, duration_seconds, view_count, published_at, image

Aggregate:
- Total views (sum view_count)
- Content mix (by duration, topic)
- Publishing frequency (by published_at)

Use query_cache(cache_key, aggregate={"field": "view_count", "op": "sum"})
to get total views.
```

2. **Viewer Engagement Analysis**
```
For recent videos:
  execute("youtube", "video", "video", {"video": "{video_id}"})
  → Fields: id, url, title, description, author, duration_seconds, view_count, subtitles

  execute("youtube", "video", "video_comments", {"video": "{video_id}", "count": 200})
  → Fields: id, text, author, published_at, like_count, reply_count, reply_level
  → Analyze commenter patterns

Use get_page(cache_key, offset=10, limit=10) to load more comments.
```

3. **Audience Demographics from Comments**
```
From comments analyze:
- Questions asked (knowledge level)
- Topics discussed (interests)
- Language and tone
- Technical depth

Use query_cache(cache_key, conditions=[{"field": "text", "op": "contains", "value": "?"}])
to filter questions from comments.

Use query_cache(cache_key, sort_by={"field": "like_count", "order": "desc"})
to find most popular comments.
```

4. **Content Performance by Audience**
```
Correlate:
- High-view videos → audience interests
- High-comment videos → engagement topics

Use query_cache(cache_key, sort_by={"field": "view_count", "order": "desc"})
to rank videos by performance metrics.
```

**Expected Output**:
- Viewer interest profile
- Engagement drivers
- Content optimization insights
- Audience knowledge level

### Workflow 3: LinkedIn Audience Profiling

**Steps**:

1. **Get Post History**
```
execute("linkedin", "post", "get_user_posts", {"user": "{alias}", "count": 50})
```

2. **Analyze Engagement**
```
For each post:
- Reaction count and types
- Comment depth
- Share count
- Post reach indicators

Use query_cache(cache_key, sort_by={"field": "reactions", "order": "desc"})
to find most engaging posts.
```

3. **Profile Engagers** (if accessible)
```
From reactions/comments:
- Job titles
- Industries
- Companies
- Seniority levels

Use execute("linkedin", "user", "get", {"user": "{engager_alias}"})
to get full profiles of top engagers.
```

4. **Content-Audience Mapping**
```
Correlate:
- Which topics get most engagement
- Which formats perform best
- Which audiences engage with what
- When different audiences are active

Use query_cache(cache_key, aggregate={"field": "reactions", "op": "avg"}, group_by="post_type")
to analyze performance by content type.
```

**Expected Output**:
- Professional audience profile
- Engagement patterns by topic
- Content-audience fit analysis
- Posting optimization recommendations

## MCP Tools Reference

### v2 Meta-Tools

| Tool | Purpose |
|------|---------|
| `discover(source, category)` | Learn available endpoints and params before execute |
| `execute(source, category, endpoint, params)` | Fetch data — replaces all v1 tools |
| `get_page(cache_key, offset, limit)` | Load more items from previous execute |
| `query_cache(cache_key, conditions, sort_by, aggregate, group_by)` | Filter/sort/aggregate cached data |
| `export_data(cache_key, format)` | Export dataset as CSV/JSON/JSONL |

### Instagram Endpoints

| Endpoint | Call | Key Params |
|----------|------|------------|
| Profile | `execute("instagram", "user", "user", {"user": "..."})` | `user` (alias/ID/URL) |
| Followers/Following | `execute("instagram", "user", "user_friendships", {"user": "...", "count": N, "type": "followers"})` | `user`, `count`, `type` (followers\|following) |
| User Posts | `execute("instagram", "user", "user_posts", {"user": "...", "count": N})` | `user`, `count` |
| User Reels | `execute("instagram", "user", "user_reels", {"user": "...", "count": N})` | `us